SAP-C02 Continuous Improvement for Existing Solutions Practice Question
A company has a serverless application that uses AWS Lambda functions. The functions are invoked by Amazon API Gateway and write to an Amazon DynamoDB table. The company wants to improve the existing solution to reduce latency for read-heavy workloads and reduce DynamoDB costs. The application reads the same items repeatedly. Which solution meets these requirements with the LEAST development effort?
⚠ Common exam trap
The trap here is assuming that increasing read capacity units reduces latency for repeated reads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable DynamoDB Accelerator (DAX) for the table and modify the Lambda functions to use the DAX client.
DAX is an in-memory cache specifically designed for DynamoDB, providing microsecond read latency and reducing the need to provision high read capacity. Integrating DAX requires only changing the DynamoDB client in the Lambda functions to the DAX client, which is a minimal code change. This directly addresses the read-heavy workload by caching frequently accessed items, lowering both latency and DynamoDB read costs. Other options either increase costs, require more development effort, or do not solve the latency and cost issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable DynamoDB Accelerator (DAX) for the table and modify the Lambda functions to use the DAX client.
Why this is correct
DAX is a fully managed, in-memory cache for DynamoDB that provides microsecond latency for read-heavy workloads. By using the DAX client in the Lambda functions, reads are served from the cache, reducing latency and lowering DynamoDB read capacity costs. The development effort is minimal because it only requires changing the DynamoDB client to the DAX client, and DAX is compatible with the existing DynamoDB API. This meets the requirements effectively.
- ✗
Increase the read capacity units (RCUs) of the DynamoDB table to handle the read load.
Why it's wrong here
Increasing RCUs can improve throughput but does not reduce latency for repeated reads and increases costs. It does not address the inefficiency of reading the same items repeatedly from the database. This solution does not meet the cost reduction goal and may not significantly improve latency for cached data. It requires no development effort but fails to meet the requirements.
- ✗
Implement an Amazon ElastiCache for Redis cluster and modify the Lambda functions to cache reads from DynamoDB.
Why it's wrong here
ElastiCache for Redis can cache data, but it requires significant development effort to implement caching logic, handle cache invalidation, and manage the Redis cluster. It also adds operational overhead. While it can reduce latency and cost, it is not the least development effort compared to DAX, which is purpose-built for DynamoDB and requires minimal code changes.
- ✗
Use Amazon DynamoDB global tables to replicate the table to multiple Regions and direct reads to the nearest Region.
Why it's wrong here
Global tables provide multi-Region replication for disaster recovery and low-latency global reads, but they do not reduce latency for repeated reads within a Region. They also increase costs due to replication and do not address the cost reduction goal. This solution adds complexity and does not meet the requirements for reducing latency and cost for read-heavy workloads.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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